Application of artificial intelligence using a convolutional neural network for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging.
J Gastroenterol Hepatol
; 36(2): 482-489, 2021 Feb.
Article
en En
| MEDLINE
| ID: mdl-32681536
ABSTRACT
BACKGROUND AND AIM:
Magnifying endoscopy with narrow-band imaging (ME-NBI) has made a huge contribution to clinical practice. However, acquiring skill at ME-NBI diagnosis of early gastric cancer (EGC) requires considerable expertise and experience. Recently, artificial intelligence (AI), using deep learning and a convolutional neural network (CNN), has made remarkable progress in various medical fields. Here, we constructed an AI-assisted CNN computer-aided diagnosis (CAD) system, based on ME-NBI images, to diagnose EGC and evaluated the diagnostic accuracy of the AI-assisted CNN-CAD system.METHODS:
The AI-assisted CNN-CAD system (ResNet50) was trained and validated on a dataset of 5574 ME-NBI images (3797 EGCs, 1777 non-cancerous mucosa and lesions). To evaluate the diagnostic accuracy, a separate test dataset of 2300 ME-NBI images (1430 EGCs, 870 non-cancerous mucosa and lesions) was assessed using the AI-assisted CNN-CAD system.RESULTS:
The AI-assisted CNN-CAD system required 60 s to analyze 2300 test images. The overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the CNN were 98.7%, 98%, 100%, 100%, and 96.8%, respectively. All misdiagnosed images of EGCs were of low-quality or of superficially depressed and intestinal-type intramucosal cancers that were difficult to distinguish from gastritis, even by experienced endoscopists.CONCLUSIONS:
The AI-assisted CNN-CAD system for ME-NBI diagnosis of EGC could process many stored ME-NBI images in a short period of time and had a high diagnostic ability. This system may have great potential for future application to real clinical settings, which could facilitate ME-NBI diagnosis of EGC in practice.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Neoplasias Gástricas
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Inteligencia Artificial
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Endoscopía Gastrointestinal
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Redes Neurales de la Computación
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Detección Precoz del Cáncer
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Imagen de Banda Estrecha
Tipo de estudio:
Diagnostic_studies
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Prognostic_studies
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Screening_studies
Límite:
Adult
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Aged
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Aged80
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
J Gastroenterol Hepatol
Asunto de la revista:
GASTROENTEROLOGIA
Año:
2021
Tipo del documento:
Article
País de afiliación:
Japón